Abstract: Aiming at the problem of error in single Lidar detection of horizontal wind field in civil aviation airport area, a support vector regression based model for estimating horizontal wind field of double Lidars was proposed. The model was based on the wind speed of two Lidars overlapping scanning regions, and the horizontal wind speed at the intersection points was used to estimate the horizontal wind speed of other data points in the radial direction. Firstly, the three characteristics of radial wind speed, horizontal wind speed and distance in the overlapping area were extracted. The overlapping area data points were used as the training set. After the same dimension was normalized, the penalty factor and kernel function parameters were set, and the initial estimated value was obtained by support vector regression. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. The estimated results were extended to a new training set, and the training set was gradually expanded to estimate the horizontal wind speed in the non-overlapping area. Finally, the error of the stepwise estimation of the method was analyzed by the measured data. The influence of wind speed and echo signal-to-noise ratio on the estimation performance of the method was analyzed. The results show that the root mean square error of the wind field estimated by the method is better than that of the single radar. The method expands the range of the horizontal wind field detected by the dual Lidars and improves the utilization of the Lidars.